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get_data.py
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get_data.py
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import os
from os.path import join
import glob
import json
import numpy as np
import cv2
import trimesh
import torch
import smplx
import nvdiffrast.torch as dr
from models.utils import load_K_Rt_from_P
from models.get_rays import get_ray_directions, get_rays
mano_layer = {'right': smplx.create('./', 'mano', use_pca=False, is_rhand=True),
'left': smplx.create('./', 'mano', use_pca=False, is_rhand=False)}
# fix MANO shapedirs of the left hand bug (https://github.com/vchoutas/smplx/issues/48)
if torch.sum(torch.abs(mano_layer['left'].shapedirs[:,0,:] - mano_layer['right'].shapedirs[:,0,:])) < 1:
print('Fix shapedirs bug of MANO')
mano_layer['left'].shapedirs[:,0,:] *= -1
def img_contrast_bright(img, a, b, g):
h, w, c = img.shape
blank = np.zeros([h, w, c], img.dtype)
dst = cv2.addWeighted(img, a, blank, b, g)
return dst
def img_adjust(img):
stone_gama = np.power(img.astype(np.float32), 0.75) # 图像较暗，若采用幂率变换，γ<1，拉伸低灰度级,交互式选择
temp = stone_gama - np.min(stone_gama)
stone_gama = temp / np.max(temp)
img_cmy = 1 - cv2.cvtColor(stone_gama, cv2.COLOR_BGR2RGB)
c, m, y = cv2.split(img_cmy)
# print(m.shape)
m_gama = np.power(m.astype(np.float32), 0.88) # 深红色较多，压缩一下
temp_m = m_gama - np.min(m_gama)
m_gama = (temp_m / (np.max(temp_m)))
out_stone = 1 - cv2.merge((c, m_gama, y))
adjusted = cv2.addWeighted(out_stone * 255, 1.3, out_stone * 255, 0, 5) / 255
return cv2.cvtColor(adjusted, cv2.COLOR_RGB2BGR)
def get_interhand_seqdatabyframe(data_path, res=(334, 512), data_name='0003_fake_gun',
capture_name='Capture9', drop_cam=[], split='train', cam_id=None, test_num=30,
return_ray=False, num_frame=20, adjust=True):
mano_layer['right'] = mano_layer['right'].cpu()
mano_layer['left'] = mano_layer['left'].cpu()
capture_idx = capture_name.replace('Capture', '')
with open(join(data_path, 'annotations/%s' % split, 'InterHand2.6M_%s_camera.json' % split)) as f:
cam_params = json.load(f)
with open(join(data_path, 'annotations/%s' % split, 'InterHand2.6M_%s_MANO_NeuralAnnot.json' % split)) as f:
mano_params = json.load(f)
cam_param = cam_params[capture_idx]
if data_name == 'all':
data_names = sorted(os.listdir(join(data_path, 'images/%s' % split, capture_name)))[:5]
else:
data_names = [data_name]
imgs_t, grayimgs_t, masks_t, w2cs_t, projs_t, mano_out_t = [], [], [], [], [], []
for data_name in data_names:
if cam_id is None or len(cam_id) == 0:
camera_names = [i for i in sorted(os.listdir(join(data_path, 'images/%s' % split, capture_name, data_name)))
if i not in drop_cam and '400' in i][:test_num]
else:
camera_names = [cam_id]
num = len(camera_names)
img_names = sorted(os.listdir(join(data_path, 'images/%s' % split, capture_name, data_name, camera_names[0])))
img_names = img_names[::max(len(img_names) // num_frame, 1)][:num_frame]
print('image views num: %d, frames num: %d' % (num, len(img_names)))
print(data_name, img_names, camera_names)
for img_name in img_names:
mano_param = mano_params[capture_idx][str(int(img_name[5:-4]))]
vertices = []
faces = []
mano_out = []
for hand_type in ['left', 'right']:
if mano_param[hand_type] is not None:
mano_pose = torch.FloatTensor(mano_param[hand_type]['pose']).view(-1, 3)
root_pose = mano_pose[0].view(1, 3)
hand_pose = mano_pose[1:, :].view(1, -1)
shape = torch.FloatTensor(mano_param[hand_type]['shape']).view(1, -1)
trans = torch.FloatTensor(mano_param[hand_type]['trans']).view(1, 3)
output = mano_layer[hand_type](global_orient=root_pose, hand_pose=hand_pose, betas=shape,
transl=trans)
vertices.append(output.vertices)
mano_out.append({'type': hand_type, 'pose': mano_pose, 'shape': shape, 'trans': trans})
if len(faces) == 0:
faces.append(mano_layer[hand_type].faces)
else:
faces.append(mano_layer[hand_type].faces + output.vertices.shape[1])
vertices = torch.cat(vertices, 1).cuda()
faces = np.concatenate(faces, 0)
# mesh = trimesh.Trimesh(vertices=vertices[0].detach().cpu().numpy(), faces=faces)
# mesh.export('test.obj')
faces = torch.from_numpy(faces.astype(np.int32)).int().cuda()
w2cs = []
projs = []
imgs = []
grayimgs = []
for i, cam_name in enumerate(camera_names):
cam_idx = cam_name.replace('cam', '')
t, R = np.array(cam_param['campos'][str(cam_idx)], dtype=np.float32).reshape(3), np.array(
cam_param['camrot'][str(cam_idx)], dtype=np.float32).reshape(3, 3)
scale_mats = np.eye(4)
scale_mats[:3, :3] = R
cam_t = -np.dot(R, t.reshape(3, 1)).reshape(3) / 1000
scale_mats[:3, 3] = cam_t
focal = np.array(cam_param['focal'][cam_idx], dtype=np.float32).reshape(2)
princpt = np.array(cam_param['princpt'][cam_idx], dtype=np.float32).reshape(2)
cameraIn = np.array([[focal[0], 0, princpt[0]],
[0, focal[1], princpt[1]],
[0, 0, 1]])
P = cameraIn @ scale_mats[:3]
proj, w2c = load_K_Rt_from_P(P[:3])
proj[0, 0] = proj[0, 0] / (res[0] / 2.)
proj[0, 2] = proj[0, 2] / (res[0] / 2.) - 1.
proj[1, 1] = proj[1, 1] / (res[1] / 2.)
proj[1, 2] = proj[1, 2] / (res[1] / 2.) - 1.
proj[2, 2] = 0.
proj[2, 3] = -0.1
proj[3, 2] = 1.
proj[3, 3] = 0.
projs.append(proj.astype(np.float32))
w2cs.append(w2c.astype(np.float32))
img = cv2.imread(
join(data_path, 'images/%s' % split, capture_name, data_name, 'cam' + cam_idx, img_name))
img = img_adjust(img) * 255 if adjust else img
# img = img_contrast_bright(img, 1.2, -0.2, 30)
grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, res)
grayimg = cv2.resize(grayimg, res)
img = torch.from_numpy((img / 255.)).float()
grayimg = torch.from_numpy((grayimg / 255.)).float()
imgs.append(img)
grayimgs.append(grayimg)
w2cs = torch.from_numpy(np.stack(w2cs)).permute(0, 2, 1).cuda()
projs = torch.from_numpy(np.stack(projs)).permute(0, 2, 1).cuda()
glctx = dr.RasterizeGLContext()
vertsw = torch.cat([vertices, torch.ones_like(vertices[:, :, 0:1])], axis=2).expand(num, -1, -1)
rot_verts = torch.einsum('ijk,ikl->ijl', vertsw, w2cs)
proj_verts = torch.einsum('ijk,ikl->ijl', rot_verts, projs)
rast_out, _ = dr.rasterize(glctx, proj_verts, faces, resolution=(res[1], res[0]))
feat = torch.ones_like(vertsw[:, :, :1])
feat, _ = dr.interpolate(feat, rast_out, faces)
masks = feat[:, :, :, :1].contiguous().squeeze(-1)
# masks = dr.antialias(masks, rast_out, proj_verts, faces).squeeze(-1)
imgs = torch.stack(imgs, dim=0)
grayimgs = torch.stack(grayimgs, dim=0)
imgs[masks == 0] = 0
grayimgs[masks == 0] = 0
imgs_t.append(imgs)
grayimgs_t.append(grayimgs)
masks_t.append(masks)
w2cs_t.append(w2cs)
projs_t.append(projs)
mano_out_t.append(mano_out)
imgs_t = torch.stack(imgs_t, dim=0)
grayimgs_t = torch.stack(grayimgs_t, dim=0)
masks_t = torch.stack(masks_t, dim=0).cpu()
w2cs_t = torch.stack(w2cs_t, dim=0).cpu()
projs_t = torch.stack(projs_t, dim=0).cpu()
hand_types = []
pose_t = []
shape_t = []
trans_t = []
for mano_out in mano_out_t:
if len(mano_out) == 2:
hand_types = ['left', 'right']
pose_t.append(torch.cat([mano_out[0]['pose'], mano_out[1]['pose']], 0).unsqueeze(0))
shape_t.append(torch.cat([mano_out[0]['shape'], mano_out[1]['shape']], 1))
trans_t.append(torch.cat([mano_out[0]['trans'], mano_out[1]['trans']], 1))
else:
hand_types = [mano_out[0]['type']]
pose_t.append(mano_out[0]['pose'].unsqueeze(0))
shape_t.append(mano_out[0]['shape'])
trans_t.append(mano_out[0]['trans'])
pose_t = torch.cat(pose_t, 0)
shape_t = torch.cat(shape_t, 0)
trans_t = torch.cat(trans_t, 0)
if return_ray:
ray_directions = []
c2ws = torch.inverse(w2cs)
for i, cam_name in enumerate(camera_names):
cam_idx = cam_name.replace('cam', '')
cam_ray_direction = get_ray_directions(res[1], res[0], cam_param['focal'][cam_idx][0],
cam_param['focal'][cam_idx][1],
cam_param['princpt'][cam_idx][0],
cam_param['princpt'][cam_idx][1], ).cuda()
tmp_ray_direction, _ = get_rays(cam_ray_direction, c2ws[i])
ray_direction = tmp_ray_direction.reshape(res[1], res[0], 3).cpu()
ray_directions.append(ray_direction)
ray_directions = torch.stack(ray_directions)
return imgs_t, grayimgs_t, masks_t, w2cs_t, projs_t, pose_t, shape_t, trans_t, hand_types, ray_directions
return imgs_t, grayimgs_t, masks_t, w2cs_t, projs_t, pose_t, shape_t, trans_t, hand_types
def get_interhand_test_seqdatabyframe(data_path, res=(334, 512), data_name='0003_fake_gun',
capture_name='Capture9', drop_cam=[], split='train', cam_id=None, test_num=30,
return_ray=False, adjust=True):
mano_layer['right'] = mano_layer['right'].cpu()
mano_layer['left'] = mano_layer['left'].cpu()
capture_idx = capture_name.replace('Capture', '')
with open(join(data_path, 'annotations/%s' % split, 'InterHand2.6M_%s_camera.json' % split)) as f:
cam_params = json.load(f)
with open(join(data_path, 'annotations/%s' % split, 'InterHand2.6M_%s_MANO_NeuralAnnot.json' % split)) as f:
mano_params = json.load(f)
cam_param = cam_params[capture_idx]
if data_name == 'all':
data_names = sorted(os.listdir(join(data_path, 'images/%s' % split, capture_name)))[:20]
else:
data_names = [data_name]
imgs_t, grayimgs_t, masks_t, w2cs_t, projs_t, mano_out_t = [], [], [], [], [], []
for data_name in data_names:
if cam_id is None:
camera_names = [i for i in sorted(os.listdir(join(data_path, 'images/%s' % split, capture_name, data_name)))
if i not in drop_cam and '400' in i][:test_num:10]
else:
camera_names = [cam_id]
num = len(camera_names)
img_names = sorted(os.listdir(join(data_path, 'images/%s' % split, capture_name, data_name, camera_names[0])))[
:]
print('image views num: %d, frames num: %d' % (num, len(img_names)))
# print(data_name, img_names, camera_names)
for img_name in img_names:
mano_param = mano_params[capture_idx][str(int(img_name[5:-4]))]
mano_out = []
for hand_type in ['left', 'right']:
if mano_param[hand_type] is not None:
mano_pose = torch.FloatTensor(mano_param[hand_type]['pose']).view(-1, 3)
shape = torch.FloatTensor(mano_param[hand_type]['shape']).view(1, -1)
trans = torch.FloatTensor(mano_param[hand_type]['trans']).view(1, 3)
mano_out.append({'type': hand_type, 'pose': mano_pose, 'shape': shape, 'trans': trans})
w2cs = []
projs = []
imgs = []
grayimgs = []
for i, cam_name in enumerate(camera_names):
cam_idx = cam_name.replace('cam', '')
t, R = np.array(cam_param['campos'][str(cam_idx)], dtype=np.float32).reshape(3), np.array(
cam_param['camrot'][str(cam_idx)], dtype=np.float32).reshape(3, 3)
scale_mats = np.eye(4)
scale_mats[:3, :3] = R
cam_t = -np.dot(R, t.reshape(3, 1)).reshape(3) / 1000
scale_mats[:3, 3] = cam_t
focal = np.array(cam_param['focal'][cam_idx], dtype=np.float32).reshape(2)
princpt = np.array(cam_param['princpt'][cam_idx], dtype=np.float32).reshape(2)
cameraIn = np.array([[focal[0], 0, princpt[0]],
[0, focal[1], princpt[1]],
[0, 0, 1]])
P = cameraIn @ scale_mats[:3]
proj, w2c = load_K_Rt_from_P(P[:3])
proj[0, 0] = proj[0, 0] / (res[0] / 2.)
proj[0, 2] = proj[0, 2] / (res[0] / 2.) - 1.
proj[1, 1] = proj[1, 1] / (res[1] / 2.)
proj[1, 2] = proj[1, 2] / (res[1] / 2.) - 1.
proj[2, 2] = 0.
proj[2, 3] = -0.1
proj[3, 2] = 1.
proj[3, 3] = 0.
projs.append(proj.astype(np.float32))
w2cs.append(w2c.astype(np.float32))
img = cv2.imread(
join(data_path, 'images/%s' % split, capture_name, data_name, 'cam' + cam_idx, img_name))
img = img_adjust(img) * 255 if adjust else img
# img = img_contrast_bright(img, 1.2, -0.2, 30)
grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, res)
grayimg = cv2.resize(grayimg, res)
img = torch.from_numpy((img / 255.)).float().cuda()
grayimg = torch.from_numpy((grayimg / 255.)).float().cuda()
imgs.append(img)
grayimgs.append(grayimg)
w2cs = torch.from_numpy(np.stack(w2cs)).permute(0, 2, 1).cuda()
projs = torch.from_numpy(np.stack(projs)).permute(0, 2, 1).cuda()
imgs = torch.stack(imgs, dim=0)
grayimgs = torch.stack(grayimgs, dim=0)
imgs_t.append(imgs)
grayimgs_t.append(grayimgs)
w2cs_t.append(w2cs)
projs_t.append(projs)
mano_out_t.append(mano_out)
imgs_t = torch.stack(imgs_t, dim=0)
grayimgs_t = torch.stack(grayimgs_t, dim=0)
w2cs_t = torch.stack(w2cs_t, dim=0)
projs_t = torch.stack(projs_t, dim=0)
hand_types = []
pose_t = []
shape_t = []
trans_t = []
for mano_out in mano_out_t:
if len(mano_out) == 2:
hand_types = ['left', 'right']
pose_t.append(torch.cat([mano_out[0]['pose'], mano_out[1]['pose']], 0).unsqueeze(0))
shape_t.append(torch.cat([mano_out[0]['shape'], mano_out[1]['shape']], 1))
trans_t.append(torch.cat([mano_out[0]['trans'], mano_out[1]['trans']], 1))
else:
hand_types = [mano_out[0]['type']]
pose_t.append(mano_out[0]['pose'].unsqueeze(0))
shape_t.append(mano_out[0]['shape'])
trans_t.append(mano_out[0]['trans'])
pose_t = torch.cat(pose_t, 0).cuda()
shape_t = torch.cat(shape_t, 0).cuda()
trans_t = torch.cat(trans_t, 0).cuda()
if return_ray:
ray_directions = []
c2ws = torch.inverse(w2cs)
for i, cam_name in enumerate(camera_names):
cam_idx = cam_name.replace('cam', '')
cam_ray_direction = get_ray_directions(res[1], res[0], cam_param['focal'][cam_idx][0],
cam_param['focal'][cam_idx][1],
cam_param['princpt'][cam_idx][0],
cam_param['princpt'][cam_idx][1], ).cuda()
tmp_ray_direction, _ = get_rays(cam_ray_direction, c2ws[i])
ray_direction = tmp_ray_direction.reshape(res[1], res[0], 3)
ray_directions.append(ray_direction)
ray_directions = torch.stack(ray_directions)
return imgs_t, grayimgs_t, masks_t, w2cs_t, projs_t, pose_t, shape_t, trans_t, hand_types, ray_directions, img_names
return imgs_t, grayimgs_t, w2cs_t, projs_t, pose_t, shape_t, trans_t, hand_types, img_names